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Data Reconciliation for On-Prem to AWS to Elasticsearch in FHIR

Author(s) Goutham Bilakanti
Country India
Abstract The Faster adoption of cloud infrastructures within the healthcare sector also needs very dependable data reconciliation mechanisms to seamlessly integrate on-premises deployments, AWS clouds, and Elasticsearch for the utilization of FHIR. This research is examining an artificial intelligence-based process that is utilized to provide improved data migration accuracy, consistency, and integrity in the healthcare sector. The methodology presented here checks the data through various phases to guarantee the minimization of discrepancies and compliance with industry standards like HIPAA. This process is minimized by using automated verification, artificial intelligence-driven data mapping, and real-time tracking, reducing errors, and improving inter-operability with other healthcare systems. AWS capabilities such as AWS Glue and Lambda functions work together to make data transformation and synchronization affordable and ensure that unstructured and structured medical records retain their fidelity upon migration. Elasticsearch enables support for advanced questioning and analytics as well, thus providing decision support to healthcare providers. This research emphasizes the importance of AI in reducing data consistency threats, thus enhancing patient data accessibility, security, and interoperability in contemporary healthcare environments.
Keywords Healthcare data reconciliation, cloud migration on AWS, FHIR implementation, AI-based data validation, Elasticsearch integration, regulatory compliance, data integrity, healthcare interoperability, cloud-based data mapping, cloud-based healthcare systems
Field Computer > Data / Information
Published In Volume 2, Issue 12, December 2021
Published On 2021-12-08
Cite This Data Reconciliation for On-Prem to AWS to Elasticsearch in FHIR - Goutham Bilakanti - IJLRP Volume 2, Issue 12, December 2021. DOI 10.5281/zenodo.15196797
DOI https://doi.org/10.5281/zenodo.15196797
Short DOI https://doi.org/g9d64q
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